Search

By the distributed averaging problem is meant the problem of computing the average value of a set of numbers possessed by the agents in a distributed network using only communication between neighboring agents. Gossiping is a well-known approach to the problem which seeks to iteratively arrive at a solution by allowing each agent to interchange information with at most one neighbor at each iterative step. Crafting a gossiping protocol which accomplishes this is challenging because gossiping is an inherently collaborative process which can lead to deadlocks unless careful precautions are taken to ensure that it does not. Many gossiping protocols are request-based which means simply that a gossip between two agents will occur whenever one of the two agents accepts a request to gossip placed by the other. In this paper, we present three deterministic request-based protocols. We show by example that the first can deadlock. The second is guaranteed to avoid deadlocks by exploiting the idea of local ordering together with the notion of an agent’s neighbor queue; the protocol requires the simplest queue updates, which provides an in-depth understanding of how local ordering and queue updates avoid deadlocks. It is shown that a third protocol which uses a slightly more complicated queue update rule can lead to significantly faster convergence; a worst case bound on convergence rate is provided. [ABSTRACT FROM AUTHOR]

In this paper we discuss and discover several conservation and associated decay laws in distributed coordination control systems, in particular in formation shape control systems. Specifically, we reveal conservations of linear momentum and angular momentum for gradient-based multi-agent formation systems modelled by single integrators, and show several corresponding conservation/decay laws for double-integrator formation stabilization systems and double-integrator flocking systems, respectively. By exploiting translation and rotation symmetry properties and insights from Noether’s theorem, we further establish a multi-agent version of the relation between symmetry and conservation laws for gradient-based coordination systems derived from general potential functions, from which we generalize the conservation/decay laws to more general networked coordination control systems. The results hold in ambient spaces of any dimensions, and we focus on the 2-D and 3-D cases due to their natural interpretation as positions of agents. [ABSTRACT FROM AUTHOR]

In this paper a new distributed feedback strategy is proposed for controlling a rigid, acyclic formation of kinematic point-modeled mobile autonomous agents in the plane. The strategy makes use of a new concept called a “target point” and is applicable to any two-dimensional, acyclic formation whose underlying directed graph can be generated by a sequence of Henneberg vertex additions. It is shown that the method can cause a group of agents starting in any given initial positions in the plane to move into a prescribed formation exponentially fast provided the formation’s designated leader and first follower start in different positions. [ABSTRACT FROM AUTHOR]

This paper develops a distributed approach for inverse optimal control (IOC) in multi-agent systems. Here each agent can only communicate with certain nearby neighbors and only accesses segments of system's trajectory, which is not sufficient for the agent to solve the IOC problem alone. By introducing the concept of the data effectiveness and bridging the connection between each segment and its contribution to solving IOC, we formulate the IOC problem as a problem of achieving least-square solutions via a distributed algorithm. Simulations are provided to validate the proposed distributed IOC approach. [ABSTRACT FROM AUTHOR]

We consider a scenario where mobile agents are required to collect measurements from a geographically dispersed set of sensors and return them to a base. An agent faces a risk of destruction while traversing the environment to reach the sensors, and gets a reward for gathering a sensor measurement only if it successfully returns to base. We first consider the single agent scenario and characterize several properties of the optimal policy for the agent based on a quantity that we term the "reward-to-risk" ratio of the tasks. We provide the optimal policy when the risk of destruction is sufficiently high, and then extend our analysis to multiple (heterogeneous) agents, with potentially different costs and survival probabilities. In particular, when the risk of destruction is sufficiently high, we show that the multi-agent task allocation problem has a submodular structure, and accordingly provide a greedy algorithm that is guaranteed to return an allocation that is within a 1 2 factor of optimal. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which results from the Koopman operator and deep neural networks. Analysis of the approximation error between states of the NTVS and the resulting LTVS is presented. Simulations on a representative NTVS show that the proposed method achieves small approximation errors, even when the system changes rapidly. Furthermore, simulations in an example of quadcopters demonstrate the computational efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]